Identification of the Residual Value Risk Factors for Road PPP Projects in China: Questionnaire Survey and Analysis

Abstract

With the increasing use of Public-Private Partnership (PPP) mode in road construction, the discrepancy between residual value and agreement of PPP projects in the transition phase has become a prominent problem. Though the risk management of road PPP projects has been lucubrated, systematic research of residual value of them are still in a state of blank. Research described in this paper aims to identify the residual value risk (RVR) factors of road PPP projects and find out the key factors of them. RVR factors of road PPP projects were first identified through the method of literature collection. In this research, 29 RVR factors were identified which were divided into 4 packages. The importance degree of each factor was then presented via descriptive analysis and confirmatory factor analysis based on the data of a questionnaire survey which explored PPP experts' perceptions of 29 factors. The order of the importance degree of each factor indicates that the incidence of residual risks within road PPP projects is strongly influenced by organizational risk, design risk, construction costs, quality of projects, operating costs, quality of service, maintenance costs, market demand and toll pricing. These 29 factors can be used to recognize the risk source and reduce the incidence of residual risks of road PPP projects.

Bridge Quality Appraisal Methodology: Application in The Strimonas Bridge. Case Study

“…1. importance rating of the g PIs (g = {1, 2, …, l}) connected to each of the h KPIs (h = {1, 2, …, m}) using a 5-point Likert scale, ranging from 1 (not important) to 5 (very important); for such rating, the input is solicited from k (k = {1, 2, …, n}) experts appropriately, such as with a questionnaire survey (Shao, Yuan, & Li, 2017); 2. processing of the input of each of the k experts via the Row Geometric Mean Method (RGMM) variation of the Analytical Hierarchy Process (AHP) (Ishizaka & Labib, 2011), to calculate each of w PI k gh , , which is the relative weight of the g th PI corresponding to the h th KPI according to the k th expert; 3. consolidation of all w PI k gh , for all n experts to calculate W PI gh , namely the relative weight of the g th PI corresponding to the h th KPI according to all experts, via an AHP results consolidation methodology developed by Goepel (2013) that incorporates the Eigenvector Method (EVM) variation of the AHP (Alonso & Lamata, 2006), and the Weighted Geometric Mean Method (WGMM) (Xu, 2000). As mentioned above, the same procedure is implemented to calculate W KPI hu (the relative weight of the h th KPI connected to the u th bridge component), and W comp u (the relative weight of the u th component about the bridge as a whole), where u = {1, 2, …, v}.…”

An Analytic Network Process Based Risk Assessment Model for PPP Hydropower Investments

“…The risk identification process is the initial and most important step in risk assessment (Yu et al, 2018). Several studies solely concentrate on identifying the risk factors for different types of PPP projects in different countries (e.g., Ghorbani et al, 2014;Chan et al, 2011;Wang et al, 2000;Osei-Kyei & Chan, 2015;Thomas et al, 2006;Shao et al, 2016;Yuan et al, 2008;Tang et al, 2015). For instance, Ghorbani et al (2014) identified risk factors for PPP highway projects in Iran.…”

Risk assessment of solar energy investments

“…Several research studies were performed to identify the risk factors that affect different types of PPP projects in different countries [18][19][20][21][22][23][24][25][26]. For example, Al-Azemi et al [25] identified the risk factors for PPP projects in Kuwait.…”